Mapping water ecosystem services: Evaluating InVEST model predictions in data scarce regions

Sustainable management of water ecosystem services requires reliable information to support decision making. We evaluate the performance of the InVEST Seasonal Water Yield Model (SWYM) against water monitoring records in 224 catchments in southern Chile. We run the SWYM in three years (1998, 2007 an...

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Published in:	Environmental modelling & software : with environment data news Vol. 138; p. 104982
Main Authors:	Benra, F., De Frutos, A., Gaglio, M., Álvarez-Garretón, C., Felipe-Lucia, M., Bonn, A.
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 01.04.2021 Elsevier Science Ltd
Subjects:	Blue ecosystem services Catchments Chile computer software Data scarce regions Decision making Ecosystem management Ecosystem service model Ecosystem services ecosystems Land use land use change Performance evaluation Predictions rain Rainfall snow South America Stream discharge Stream flow Sustainability management Temporal variations Water management Water monitoring Water regulation Water supply Water yield Chile South America Data scarce regions Ecosystem service model Water regulation Water supply Blue ecosystem services
ISSN:	1364-8152, 1873-6726
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Abstract	Sustainable management of water ecosystem services requires reliable information to support decision making. We evaluate the performance of the InVEST Seasonal Water Yield Model (SWYM) against water monitoring records in 224 catchments in southern Chile. We run the SWYM in three years (1998, 2007 and 2013) to account for recent land-use change and climatic variations. We computed squared Pearson correlations between SWYM monthly quickflow predictions and streamflow observations and applied a generalized mixed‐effects model to evaluate annual estimations. Results show relatively low monthly correlations with marked latitudinal and temporal variations while annual estimates show a good match between observed and modeled values, especially for values under 1000 mm/year. Better predictions were observed in regions with high rainfall and in dry years while poorer predictions were found in snow dominated and drier regions. Our results improve SWYM performance and contribute to water supply and regulation decision-making, particularly in data scarce regions. [Display omitted] •InVEST Seasonal Water Yield Model (SWYM) is used to model monthly and annual water flows.•We compare SWYM estimations to observed streamflow data in 224 basins.•Annual estimations are more precise than monthly ones.•SWYM performs better in rainier regions and in drier years.•SWYM does not perform well in snow-dominated and arid regions.
AbstractList	Sustainable management of water ecosystem services requires reliable information to support decision making. We evaluate the performance of the InVEST Seasonal Water Yield Model (SWYM) against water monitoring records in 224 catchments in southern Chile. We run the SWYM in three years (1998, 2007 and 2013) to account for recent land-use change and climatic variations. We computed squared Pearson correlations between SWYM monthly quickflow predictions and streamflow observations and applied a generalized mixed‐effects model to evaluate annual estimations. Results show relatively low monthly correlations with marked latitudinal and temporal variations while annual estimates show a good match between observed and modeled values, especially for values under 1000 mm/year. Better predictions were observed in regions with high rainfall and in dry years while poorer predictions were found in snow dominated and drier regions. Our results improve SWYM performance and contribute to water supply and regulation decision-making, particularly in data scarce regions. Sustainable management of water ecosystem services requires reliable information to support decision making. We evaluate the performance of the InVEST Seasonal Water Yield Model (SWYM) against water monitoring records in 224 catchments in southern Chile. We run the SWYM in three years (1998, 2007 and 2013) to account for recent land-use change and climatic variations. We computed squared Pearson correlations between SWYM monthly quickflow predictions and streamflow observations and applied a generalized mixed‐effects model to evaluate annual estimations. Results show relatively low monthly correlations with marked latitudinal and temporal variations while annual estimates show a good match between observed and modeled values, especially for values under 1000 mm/year. Better predictions were observed in regions with high rainfall and in dry years while poorer predictions were found in snow dominated and drier regions. Our results improve SWYM performance and contribute to water supply and regulation decision-making, particularly in data scarce regions. [Display omitted] •InVEST Seasonal Water Yield Model (SWYM) is used to model monthly and annual water flows.•We compare SWYM estimations to observed streamflow data in 224 basins.•Annual estimations are more precise than monthly ones.•SWYM performs better in rainier regions and in drier years.•SWYM does not perform well in snow-dominated and arid regions.
ArticleNumber	104982
Author	Álvarez-Garretón, C. Felipe-Lucia, M. Benra, F. Bonn, A. Gaglio, M. De Frutos, A.
Author_xml	– sequence: 1 givenname: F. surname: Benra fullname: Benra, F. email: felipe.benra@idiv.de organization: Helmholtz Centre for Environmental Research - UFZ, Department of Ecosystem Services, Permoserstraße 15, 04318, Leipzig, Germany – sequence: 2 givenname: A. surname: De Frutos fullname: De Frutos, A. organization: German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstrasse 4, D-04103, Leipzig, Germany – sequence: 3 givenname: M. surname: Gaglio fullname: Gaglio, M. organization: Department of Life Sciences and Biotechnology, University of Ferrara, Via L. Borsari 46, 44121, Ferrara, Italy – sequence: 4 givenname: C. surname: Álvarez-Garretón fullname: Álvarez-Garretón, C. organization: Center for Climate and Resilience Research CR2, FONDAP, 15110009, Santiago, Chile – sequence: 5 givenname: M. surname: Felipe-Lucia fullname: Felipe-Lucia, M. organization: Helmholtz Centre for Environmental Research - UFZ, Department of Ecosystem Services, Permoserstraße 15, 04318, Leipzig, Germany – sequence: 6 givenname: A. surname: Bonn fullname: Bonn, A. organization: Helmholtz Centre for Environmental Research - UFZ, Department of Ecosystem Services, Permoserstraße 15, 04318, Leipzig, Germany
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Snippet	Sustainable management of water ecosystem services requires reliable information to support decision making. We evaluate the performance of the InVEST Seasonal...
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SubjectTerms	Blue ecosystem services Catchments Chile computer software Data scarce regions Decision making Ecosystem management Ecosystem service model Ecosystem services ecosystems Land use land use change Performance evaluation Predictions rain Rainfall snow South America Stream discharge Stream flow Sustainability management Temporal variations Water management Water monitoring Water regulation Water supply Water yield
Title	Mapping water ecosystem services: Evaluating InVEST model predictions in data scarce regions
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